| # Exploratory Data Analysis - Paavo Dataset |
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| An exploratory data analysis (EDA) pipeline for **Statistics Finland's Paavo open dataset**, which provides area and municipality-level statistics on demographics, income levels, household structures, and building infrastructure across Finland. |
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| ## Project Overview |
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| This script processes the Paavo dataset to generate summary statistics, distribution plots, correlation analysis, and targeted visualizations for key metrics across top-ranking postal areas. |
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| ### Key Analysis Features: |
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| * **Data Cleaning:** Strips whitespace from headers and excludes regional summary records (`MK*` rows, e.g., "MK01 Uusimaa"). |
| * **Demographic Distributions:** Analyzes key attributes such as total inhabitants, average/median income, average age, and household sizes. |
| * **Correlation Heatmap:** Generates feature relationships across all numeric metrics. |
| * **Income & Household Breakdown:** Evaluates distribution across lowest, middle, and highest income tiers for top population and household centers. |
| * **Density & Economic Power Metrics:** Computes housing density proxies (inhabitants per residential building) and analyzes top regional purchasing power. |
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| ## Directory Structure |
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| ``` |
| ├── eda_script.py # Main EDA script |
| ├── PaavoDataset.csv # Source dataset (Statistics Finland) |
| └── eda_outputs/ # Output directory generated by script |
| ├── summary_statistics.csv |
| ├── 01_distributions.png |
| ├── 02_correlation_heatmap.png |
| ├── 03_income_categories_top20.png |
| ├── 04_household_income_categories_top20.png |
| ├── 05_inhabitants_per_building_top20.png |
| └── 06_purchasing_power_top20.png |
| ``` |
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| ## Prerequisites & Installation |
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| Ensure you have Python 3.8+ installed along with the required dependencies: |
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| ```bash |
| pip install pandas matplotlib seaborn |
| ``` |
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| ## Data Input Requirements |
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| Place the raw dataset file **`PaavoDataset.csv`** in the root directory. |
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| * **Format:** Semicolon-separated values (`sep=";"`) |
| * **Encoding:** `latin1` |
| * **Key Columns Expected:** |
| * `Area` |
| * `Inhabitants`, `Average age of inhabitants`, `Average size of households` |
| * `Average income of inhabitants`, `Median income of inhabitants` |
| * `Inhabitants belonging to the lowest/middle/highest income category` |
| * `Households`, `Households belonging to the lowest/middle/highest income category` |
| * `Residential buildings`, `Accumulated purchasing power of inhabitants` |
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| --- |
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| ## Usage |
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| Run the Python analysis script: |
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| ```bash |
| python eda_script.py |
| ``` |
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| --- |
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| ## Generated Visualizations & Analytical Insights |
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| Below are the detailed logical notes, observations, and conclusions derived from each generated plot: |
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| ### 1. `01_distributions.png` - Key Demographic & Income Distributions |
| * **Inhabitants Distribution:** Highly right-skewed. The vast majority of postal code areas have small populations, while a few urban hubs house large concentrations of people. |
| * **Average vs. Median Income:** `Average income` typically exhibits a rightward tail compared to `Median income`, indicating income inequality where a small percentage of high earners elevates average regional figures. |
| * **Average Age & Household Size:** Average age shows a normal to slightly multi-modal distribution, distinguishing university/younger urban areas from aging rural communities. Household sizes cluster tightly around 1.5 to 2.5 people per household, reflecting national trends of smaller urban households. |
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| ### 2. `02_correlation_heatmap.png` - Feature Correlation Matrix |
| * **Population vs. Total Purchasing Power:** Strong positive correlation between total `Inhabitants` / `Households` and `Accumulated purchasing power`, confirming that volume drives overall market size. |
| * **Income & Age Dynamics:** Median income often correlates negatively with higher average age in rural regions, whereas prime working-age areas show elevated median income. |
| * **Housing vs. Population:** High correlation between the number of residential units and population, though variations indicate differences in housing structures (apartment-heavy urban vs. single-family rural areas). |
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| ### 3. `03_income_categories_top20.png` - Individual Income Composition (Top 20 Populated Areas) |
| * **Urban Middle-Class Dominance:** Highly populated postal codes (e.g., major urban centers in Helsinki, Tampere, Oulu) show a large proportion of inhabitants falling into the middle-income tier. |
| * **Student & Young Professional Enclaves:** Top areas containing major universities exhibit higher ratios of the lowest income category due to non-working student populations, despite high total population counts. |
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| ### 4. `04_household_income_categories_top20.png` - Household Income Composition (Top 20 Household Areas) |
| * **Single-Person Household Impact:** Household income distributions shift relative to individual income distributions because single-person households are more likely to fall into lower household-income brackets compared to dual-income families. |
| * **Socioeconomic Stratification:** Comparing suburban centers against inner-city areas reveals clear differences: suburban hub areas tend to have larger shares of upper-tier household incomes, whereas dense inner-city postal codes show a higher volume of single-income and entry-level households. |
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| ### 5. `05_inhabitants_per_building_top20.png` - Urban Housing Density |
| * **Multi-Storey Concentration:** Dense urban postal codes dominate this metric, as high-rise residential complexes house significantly more people per single residential structure building footprint. |
| * **Infrastructure Demand:** Areas ranking highest in inhabitants per building represent key focus zones for high-density municipal services, public transit routes, and commercial utility planning. |
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| ### 6. `06_purchasing_power_top20.png` - Accumulated Purchasing Power |
| * **Market Size Drivers:** Total purchasing power is heavily weighted toward high-population, high-income urban districts (primarily Uusimaa/Helsinki region). |
| * **Commercial Viability:** While some areas may have lower average incomes, their high population density compensates to produce massive aggregate purchasing capacity (EUR), making them critical target zones for retail and enterprise expansion. |
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| ## Summary of Output Artifacts |
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| All outputs are exported to the **`eda_outputs/`** directory: |
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| | Artifact | Description | |
| | :--- | :--- | |
| | `summary_statistics.csv` | Descriptive statistics (`count`, `mean`, `std`, `min`, `quartiles`, `max`) for numeric columns. | |
| | `01_distributions.png` | Distribution plots with KDE curves for key demographic and income metrics. | |
| | `02_correlation_heatmap.png` | Matrix correlation map across all numeric features. | |
| | `03_income_categories_top20.png` | Stacked horizontal bar plot of income breakdown for top 20 areas by population. | |
| | `04_household_income_categories_top20.png` | Stacked horizontal bar plot of household income breakdown for top 20 areas by households. | |
| | `05_inhabitants_per_building_top20.png` | Bar chart of top 20 areas ranked by inhabitants per residential building. | |
| | `06_purchasing_power_top20.png` | Bar chart of top 20 areas ranked by total accumulated purchasing power (EUR). | |
